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The causal mediation literature has mainly focused on “natural effects” as measures of mediation, but these have been criticized for their reliance on empirically unverifiable assumptions. They are also impossible to estimate without additional untestable assumptions in the common situation of exposure-induced mediator–outcome confounding. “Interventional effects” have been proposed as alternative measures that overcome these limitations, and 2 versions have been described for the exposure-induced confounding problem. We aim to provide insight into the interpretation of these effects, particularly by describing randomized controlled trials that could hypothetically be conducted to estimate them. In contrast with natural effects, which are defined in terms of individual-level interventions, the definitions of interventional effects rely on population-level interventions. This distinction underpins the previously described advantages of interventional effects, and reflects a shift from individual effects to more tangible population-average effects. We discuss the conceptual and practical implications for the conduct of mediation analysis. See video abstract at, http://links.lww.com/EDE/B383.

From the aClinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, Australia

bCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.

Supported by a Centre of Research Excellence grant from the Australian National Health & Medical Research Council, ID#1035261, to the Victorian Centre for Biostatistics (ViCBiostat). The Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program.

The authors report no conflicts of interest.

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